Intelligent travelling visitor estimation model with big data mining

ABSTRACT

Smart estimation modelling of tourism tourists has become aresearch challenge due to increment of tourists today. Since existing models have many limitations, asmart estimation model of traveler quantity is proposed in this paper. First, data nodes of visitors are classified by using Bayesian decision tree (BDT). Then, an analysismodel of tourism visitors is constructed. Last, asmart estimate model of tourist traveler is constructed by the implemented differential operator according to Autoregressive Integrated Moving Average model. Experimental results show that the accuracy of the proposed model is higher than traditional models.

KEYWORDS:

• Big data mining
• intelligent travelling
• tourist quantity
• estimation
• ARIMA

Acknowledgements

We want to thank Dr Cheng from Middlesex University (UK) for his kindly work in language improvement.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper is supported by Key projects of Humanities and Social Sciences in Anhui higher education institutions [SK2016A0653, SK2016A0654]; Action plan for innovation and development of Higher Vocational Education (2015–2018 years) [XM-01].